Iris recognition is one of the most powerful techniques for biometric identification ever developed. Commercial systems, like those based on an algorithm developed by Daugman, (see U.S. Pat. No. 5,291,560, the contents of which are hereby incorporated by reference herein) have been available since 1995 and have been used in a variety of practical applications.
The basic principles of iris recognition are summarized in FIG. 1. The subject iris 10 is illuminated with light from controlled and ambient light sources 12. The camera 14 and the controlled illumination 16 are at some defined standoff distance 18, 20, from the subject. The camera and lens 14 (possibly filtered) acquires an image that is then captured by a computer 22. The iris image 24 is then segmented, normalized and an iris template (commonly called an iris code) 28 is generated. Segmentation identifies the boundaries of the pupil and iris. Normalization remaps the iris region between the pupil and the sclera (the white of the eye) into a form convenient for template generation and removes the effects of pupil dilation using a suitable model. The template is then matched against an existing database 30 of previously enrolled irises; a match against the database indicates the current iris is the same iris used to create the template in the database.
However, prior iris recognition techniques suffer from several difficulties for some applications. One difficulty is that when used in systems with large (e.g., 500,000) people in the database, such systems require a large number of servers to accommodate the large number of people in the databases.
Another difficulty is that prior techniques have been designed and optimized for applications in which the false match rate is of paramount importance-neglecting applications in which other factors are more important than the false match rate or in which other engineering tradeoffs should be considered. In addition, these techniques expect a reasonably high quality image. There are applications where one would accept a higher false match rate in return for the ability to use lower quality images. Forensic applications are one example. Acquisition of images for iris recognition in less constrained environments is another example.
Acquisition of high quality iris images is difficult because the human iris is a small target (−1 cm diameter), with relatively low albedo (˜0.15), in the near IR. Existing iris recognition algorithms recommend a resolution of the order of 200 pixels across the iris. Hence, acquisition of iris images of sufficient quality for iris recognition is challenging, particularly from a distance. Current commercially available iris cameras require substantial cooperation on the part of the subject. Two simple metrics for the required degree of cooperation are the capture volume and the residence time in the capture volume. The capture volume is the three dimensional volume into which an eye must be placed and held for some period of time (the residence time) in order for the system to reliably capture an iris image of sufficient quality for iris recognition. For ease of use, the user would want the spatial extent of the capture volume to be as large as possible and the residence time to be as small as possible.
A related issue is the standoff distance, the distance between the subject and the iris image acquisition system. Existing systems require reasonably close proximity, in some cases an ‘in your face’ proximity. Existing iris recognition algorithms are generally good enough for most applications in which the subject is sufficiently cooperative. A challenge resides in reducing constraints on the subject so iris recognition is easier to use.
In scenarios in which iris recognition needs to be deployed in less constrained environments, we can reasonably expect that the acquired iris images will be of lower quality than those in highly constrained environments. Hence, there is a need for algorithms that can work with lower quality images, even at the possible expense of higher false match and/or false non-match rates.
Current iris recognition algorithms search a template database by brute force. The algorithms used are very efficient, but they conduct the search by systematically testing against every template in the database until a match is found. For large databases that are subject to high interrogation rates this consumes many CPU cycles and requires deployment of large collections of computers to provide acceptable response rates. Thus, there is a need for a method that can improve the search rate over existing algorithms and one that will decrease the equipment costs for the database searches. This will become particularly important as high throughput iris recognition systems become widely deployed.
Thus, there are multiple reasons that we need improvements to existing iris recognition methods, including alternatives to the existing methods.